Estimate Air Pollution Research Articles

Integrated emission inventory and modelling to assess the distribution of particulate matters from rice straw open burning in Hanoi, Vietnam

Rice straw open burning (RSOB) is a major environmental issue in many Asian countries, including Vietnam, during every crop season. This study integrates activity data (post-harvest rice straw) from the SAR Sentinel-1A satellite with an empirical survey to estimate air pollutants (PM10 and PM2.5) emissions from RSOB in Hanoi for the year 2020. According to the findings, Hanoi city has 125.5 thousand ha of rice cultivation area in 2020, producing 725 thousand tons of rice and leaving behind approximately 556 thousand tons of rice straw and stubble on the field. Farmers burning the rice straw open-field is a long-standing practice that emits 517.8 tons of PM10 and 472.3 tons of PM2.5 each year. The results of the ADMS dispersion model show that the maximum hourly averaged concentrations of PM10 and PM2.5 appear at 8 p.m. at values of 90.5 μg/m3 and 82.6 μg/m3 for the winter-spring crop and 16.8 μg/m3 and 15.3 μg/m3 for the summer-autumn crop, respectively. The hotspot locates most clearly at the South-Eastern part of the city, corresponding to the region with the largest crop cultivating area. Emission from RSOB has contributed to the local air pollution of Hanoi and its surrounding areas, including non-cultivation and no burning areas.

Ambient Air Pollution and Dysanapsis: Associations with Lung Function and Chronic Obstructive Pulmonary Disease in the Canadian Cohort Obstructive Lung Disease Study.

Rationale: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known about how airway abnormalities and lung growth might modify this relationship. Objectives: To evaluate the associations of ambient air pollution exposure with lung function and COPD and examine possible interactions with dysanapsis. Methods: We made use of cross-sectional postbronchodilator spirometry data from 1,452 individuals enrolled in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study with linked ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution estimates. Dysanapsis, or the ratio of the airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions. Measurements and Main Results: In adjusted models, 101.7 ml (95% confidence interval [CI], -166.2 to -37.2) and 115.0 ml (95% CI, -196.5 to -33.4) lower FEV1 were demonstrated per increase of 2.4 ug/m3 PM2.5 and 9.2 ppb NO2, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modeling the airway-to-lung ratio as a continuous variable. However, a 109.8 ml (95% CI, -209.0 to -10.5] lower FEV1 and an 87% (95% CI, 12% to 213%) higher odds of COPD were observed among individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m3 increment of PM2.5. Conclusions: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis.

Personal Exposure and Inhaled Dose Estimation of Air Pollutants during Travel between Albany, NY and Boston, MA

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, dose-exposure studies during long commutes are scarce and more information on inhaled doses is needed. This study focuses on personal exposures to size-fractionated particulate matter (PM1, PM2.5, PM4, PM7, PM10, TSP) and black carbon (BC) inside a bus traveling more than 270 kms on a highway between Albany, NY and Boston, MA. Measurements were also made indoors, outdoors, and while walking in each city. Mean PM (PM1, PM2.5, PM4, PM7, PM10, TSP) and mean BC concentrations were calculated to estimate the inhaled exposure dose. The highest average PM2.5 and PM10 exposures concentrations were 30 ± 12 and 111 ± 193 µg/m3, respectively, during Boston to Albany. Notably, personal exposure to BC on a bus from Albany to Boston (5483 ± 2099 ng/m3) was the highest measured during any commute. The average inhaled dose for PM2.5 during commutes ranged from 0.018 µg/km to 0.371 µg/km. Exposure concentrations in indoor settings (average PM2.5 = 37 ± 55 µg/m3, PM10 = 78 ± 82 µg/m3, BC = 5695 ± 1774 ng/m3) were higher than those in outdoor environments. Carpeted flooring, cooking, and vacuuming all tended to increase the indoor particulate level. A high BC concentration (1583 ± 1004 ng/m3) was measured during walking. Typical concentration profiles in long-haul journeys are presented.

Frailty Risk in Older Adults Associated With Long-Term Exposure to Ambient PM2.5 in 6 Middle-Income Countries.

BackgroundA series of studies have explored the health effects of long-term exposure to ambient PM2.5 among older adults. However, few studies have investigated the adverse effect of long-term exposure to ambient PM2.5 on frailty, and the results are inconclusive. This study sought to investigate the associations between long-term exposure to ambient PM2.5 and frailty in 6 low- and middle-income countries.MethodsWe included an analytical sample of 34 138 individuals aged 50 and older from the Study on global AGEing and adult health Wave 1 (2007/2010). Air pollution estimates were generated using a standard methodology derived from Moderate Resolution Imaging Spectroradiometer observations and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model. A 3-level hierarchical logistic model was used to evaluate the association between frailty index and long-term PM2.5 exposure at 3 levels (individual, province, and country).ResultsIn rural areas, each 10 μg/m3 increase in ambient PM2.5 was associated with a 30% increase in the odds of frailty (OR = 1.30, 95% CI: 1.21–1.39) after adjusting for various potential confounding factors. The gender-stratified analysis showed that the association seemed to be slightly stronger in men (OR = 1.31, 95% CI: 1.18–1.46) than in women (OR = 1.21, 95% CI: 1.07–1.36) in rural areas.ConclusionIn a large sample of community-based older adults from 6 middle-income countries, we found evidence that long-term PM2.5 exposure was associated with frailty in rural areas.

A Long Way from Steubenville: Environmental Epidemiology in a Rapidly Changing World.

This commentary focuses on research that has long been at the core of environmental epidemiology: studies of the health effects of air pollution. It highlights publications in the American Journal of Epidemiology going back more than 50 years that have contributed to the debate about the validity of this research and its meaning for public policy. Technological advances have greatly expanded the toolbox of environmental epidemiologists in terms of measuring and analyzing complex exposures in large populations. Yet, discussions about biases in estimating air pollution health effects have always been and remain intense. Epidemiologists have brought new methodologies and concepts to this research, alleviating some but not all concerns. Here, the focus is on seminal epidemiologic work that established valid links between air pollution exposures and health outcomes and generated data for environmental policies and prevention. With this commentary, I hope to inspire epidemiologists to address many more of the burning environmental health questions-wildfires included-with a similar scientific doggedness. The rapidly changing conditions of our planet are challenging us to innovate and offer solutions, albeit perhaps a little bit faster this time around.

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study

BackgroundRecent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people’s homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.ObjectiveThis simulation study aimed to understand the impact of incomplete addresses on health effect estimation based on the association between particulate matter with diameter ≤10 µm (PM10) and low birth weight (LBW).MethodsWe generated true annual average concentrations of PM10 at 46,007 mothers’ homes and their LBW status, using the parameters obtained from our data analysis and a previous study in Seoul, Korea. Then, we hypothesized that mothers’ address information is limited to the district and compared the properties of their health effect estimates of PM10 with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM10 and nine exposure prediction methods that provide different sets of predicted PM10 concentrations of mothers.ResultsWe observed increased bias and root mean square error consistently across all environmental scenarios and prediction methods using incomplete addresses compared to complete addresses. However, the bias related to incomplete addresses decreased when we used population-representative exposures averaged to the district from predicted PM10 at census tract centroids.SignificanceOur simulation study suggested that individual exposure estimated by prediction approaches and averaged across population-representative points can provide improved accuracy in health effect estimates when complete address data are unavailable.Impact statementOur simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people’s addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, district, and zip code area. Our findings can help understand the possible consequences of limited address information and provide practical guidance in achieving the accuracy in health effect analysis.

A license plate recognition data to estimate and visualise the restriction policy for diesel vehicles on urban air quality: A case study of Shenzhen

Diesel vehicles for road freight are primary contributors to PM2.5 and NOX emissions in numerous cities. Shenzhen, which is a megacity in China, has made efforts to promote the transition to green transport by implementing license plate restrictions. Nevertheless, it is still unclear whether the restrictions have greatly improved urban air quality. An effective framework for accurately estimating and visualising the effect of restriction policy on a large scale is still lacking. Therefore, this study aims to develop a novel method to visualise and evaluate the effect of license plate restriction policy by bridging diesel truck's license plate recognition data to emission inventories. The results reveal that the impact of the peak restriction on air quality was limited if it only affected nonlocal diesel vehicles. While the promotion of eco-friendly vehicles could reduce PM2.5 and NOX emissions. The findings could provide references for other cities or countries to estimate air pollution from diesel vehicles and recognise high emission zones at a large scale and thus create effective policies and initiatives.

Estimation of Air Pollutants using Time Series Model at Coalfield Site of India

Assessment of air pollutants and quality is an intricate task because of dynamic nature, unpredictability and high inconsistency in space and time. In this study, a time series moving average (MA) model is employed to estimate air pollutants (PM2.5, PM10, NO2, NOX, O3, SO2 and CO) over the coalfield site of India. The estimated O3 with Adj. R2 = 0.958 was identified as the most accurate estimation followed by other estimated pollutants. Though, results for the estimated PM2.5 (Adj. R2 = 0.950) and NO2 (Adj. R2 = 0.949) were found almost similar to the results of O3 (Adj. R2 = 0.958). The estimated CO with Adj. R2 = 0.887 was identified lower among all the estimated pollutants was also found very well. The existing results of the study demonstrate that MA model permits us to precisely estimate daily basis pollutant concentrations, for the different sites of India.

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Air pollution presents a serious health challenge in urban metropolises. While accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models have yet fully captured the complex interactions between the temporal characteristics of air pollution and the spatial characteristics of urban dynamics. Our proposed Deep-AIR fills this gap to provide fine-grained city-wide air pollution estimation and station-wide forecast, by exploiting domain-specific features (including Air Pollution, Weather, Urban Morphology, Transport, and Time-sensitive features), with a hybrid CNN-LSTM structure to capture the spatio-temporal features, and 1x1 convolution layers to enhance the learning of temporal and spatial interaction. Deep-AIR outperforms compatible baselines by a higher accuracy of 1.5%, 2.7%, and 2.3% for Hong Kong and 1.4%, 1.4% and 3.3% for Beijing in fine-grained 1-hr pollution estimation, and 1-hr and 24-hr forecasts, respectively. Saliency analysis reveals that for Hong Kong, spatial features, including street canyon and road density, are the best predictors for NO2, while temporal features, including historical air pollutants and weather, are the best predictors for PM2.5. For Beijing, historical air pollutant data, traffic congestion, wind direction and seasonal indicator are the best predictors for all pollutants. PM10 in Hong Kong is achieving the best estimation and forecast accuracy, whilst CO in Beijing is achieving the best results.

Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing

Air pollution is a central environmental problem in countries around the world. It contributes to climate change through the emission of greenhouse gases, and adversely impacts the health of billions of people. Despite its importance, detailed information about the spatial and temporal distribution of pollutants is complex to obtain. Ground-level monitoring stations are sparse, and approaches for modeling air pollution rely on extensive datasets which are unavailable for many locations. We introduce three techniques for the estimation of air pollution to overcome these limitations: 1) a baseline localized approach that mimics conventional land-use regression through gradient boosting; 2) an OpenStreetMap (OSM) approach with gradient boosting that is applicable beyond regions covered by detailed geographic datasets; and 3) a remote sensing-based deep learning method utilizing multiband imagery and trace-gas column density measurements from satellites. We focus on the estimation of nitrogen dioxide (NO₂), a common anthropogenic air pollutant with adverse effects on the environment and human health. Our local baseline model achieves strong results with a mean absolute error (MAE) of 5.18 &#x00B1; <inline-formula> <tex-math notation="LaTeX">$0.16~\mu \text {g/m}^{3}$ </tex-math></inline-formula> NO₂. Substituting localized inputs with OSM leads to a degraded performance (MAE 7.22 &#x00B1; 0.14) but enables NO₂ estimation at a global scale. The proposed deep learning model on remote sensing data combines high accuracy (MAE 5.5 &#x00B1; 0.14) with global coverage and heteroscedastic uncertainty quantification. Our results enable the estimation of surface-level NO₂ pollution with high spatial resolution for any location on Earth. We illustrate this capability with an out-of-distribution test set on the US westcoast. Code<xref ref-type="fn" rid="fn1">¹</xref> and data<xref ref-type="fn" rid="fn2">²</xref> are publicly available.

Communicating respiratory health risk among children using a global air quality index.

Air pollution poses a serious threat to children's respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for risk communication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25×25km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

‘Real-time’ air quality channels: A technology review of emerging environmental alert systems

Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed? Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure.

A novel multi-pollutant space-time learning network for air pollution inference

Detailed information about air pollution in space and time is essential to manage risks to public health. In this paper we propose a multi-pollutant space-time learning network (Multi-AP learning network), which estimates pixel-wise (grid-level) concentrations of multiple air pollutant species based on fixed-station measurements and multi-source urban features, including land use information, traffic data, and meteorological conditions. We infer concentrations of multiple pollutants within one integrated learning network, which is applied to and evaluated on a case study in Chengdu (4900 km2, 26 April - 12 June 2019), where air pollutant (PM2.5, PM10 and O3) measurements from 40 monitoring sites are used to train the network to estimate pollutant concentrations in 4900 grid-cells (1 km2). The Multi-AP learning network allows us to estimate highly-resolved (1 km × 1 km, hourly) air pollution maps based on pollutant measurements which cover less than 1% of the grid-cells with better accuracy compared to other approaches, and with significant computational efficiency improvements. The time-cost is 1/3 of the time-cost of modelling each pollutant individually. Furthermore, we evaluate the relative importance of features and find that the meteorological feature set is the most important followed the land use features. The proposed Multi-AP method could be used to estimate air pollution exposure across a city using a limited set of air pollution monitoring sites.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national ‘lockdown’ periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

Emission and spatialized health risks for trace elements from domestic coal burning in China

Residential coal combustion (RCC) emission exhibited obvious daily variation, while no real-time estimation of air pollutants from RCC has been reported, as the shortages of corresponding activity dataset and emission factors with high time resolution. A real-time monitoring platform for RCC emission was established. Hourly emission factors of 18 typed of TEs from eleven kinds of chunk coals and nine kinds of honeycomb coals burning in China were obtained. The monthly and hourly coal consumption amounts were calculated with reference and our field survey. Then the hourly TEs emission inventories from RCC were established in China. GEOS-Chem and Risk Quotients Models were utilized to map the spatialized health risks of hazardous elements, including the gridded hazard index and carcinogenic risk. The result indicated that the EFs of TEs would be underestimated if the tests only consider flaming conditions. Cu, K, Ca, Zn, and Co were the top five elements from RCC, with corresponding emission amounts as 1397.7, 1054.0, 676.0, 623.5 and 420 tons in 2017, respectively. K, Ti, Fe, Sn, and Sb showed hourly peak values under flaming dominated periods, accounting for 48.2%, 45.9%, 31.8%, 42.8%, and 33.8% of their daily emissions. Other elements (e.g., V, Co, As, Hg and Pb) exhibited higher emissions under smoldering dominated period in nighttime, accounting for 22.2%, 32.9%, 27.6%, 34.7%, and 28.4% of their daily emissions. TEs emission from RCC closely follows the habits of human daily cooking and heating activity. The national HI were lower than the acceptable level (HI ≤ 1) except Sichuan Province (up to 1.2). Higher carcinogenic risks (≥1 × 10-6) occurred in parts of Sichuan, Shanxi, Hunan and Hubei, which were up to 2.0 × 10-5. The high-resolution TEs emission inventories could be useful for future modeling works on the formation and evolution of air pollution and are helpful for human exposure assessment.

Hourly emission estimation of black carbon and brown carbon absorption from domestic coal burning in China

Residential coal combustion (RCC) emission demonstrates obvious daily variation, while no real-time estimation of air pollutants from RCC has been reported, as the limitation of hourly activity data and emission factors. With a dilution sampling system, a high-precision electronic balance, and an Aethalometer Model AE33, a real-time monitoring platform for RCC emission was established. Hourly emission factors (EFs) of BC and absorption emission factors (AEFs) of BrC from eleven kinds of chunk coals and nine kinds of honeycomb coals burning in China were obtained. The monthly and hourly coal consumption amounts were calculated with the activity data from literature reviews and a field survey. The first hourly BC and absorption cross section of BrC emission inventories from RCC were established in China. The historical emission trends (2003–2017) indicated that the policy has rapidly controlled the emission of BC and ACSBrC from RCC in urban area (26.7% and 31.8% decreased, respectively in 2013). While in rural areas, their emission continually increased by 1.2% ~ 5.3% until more strict law enacted in 2017. Emissions of BC and ACSBrC in winter seasons were 60.1 Gg and 1064.1 Gm2, which accounted for 54.3% and 55.1% of the total BC and ACSBrC emissions correspondingly. The peak values of hourly emission of BC and ACSBrC (in 370 nm) normally appeared at 19:00–23:00, accounting for 43.0% and 41.5% of their total daily emission. The low emission periods were at cooking times including 7:00, 12:00, and 17:00 of a day and the whole emission of BC and ACSBrC for the three periods accounted for 1.8% and 2.3% of their daily emission. This high-resolution BC and ACSBrC emission inventories can be useful for future modeling works on the formation and evolution of a haze event, the smoke aging and transportation, as well as corresponding climate and human health effects.

Development of high-resolution spatio-temporal models for ambient air pollution in a metropolitan area of China from 2013 to 2019

Modeling high-resolution air pollution concentrations is essential to accurately assess exposure for population studies. The aim of this study is to establish an advanced exposure model to predict spatiotemporal changes in fine particulate matter (PM2.5), nitrogen dioxides (NO2), and ozone (O3) concentrations in Shanghai, China. The model is constructed on a geo-statistical modeling framework that incorporates a dimension reduction regression approach and a spatial smoothing function to deal with fine-scale exposure variations. We used a dataset with comprehensive observational and predictor variables that included monitoring data from both national and local agencies from 2013 to 2019, a high-resolution geographical dataset of predictor variables, and a full-coverage weekly satellite data of the aerosol optical depth at a 1 × 1 km2 resolution. Our model performed well in terms of the spatial and temporal prediction ability assessed by cross-validation (CV) for PM2.5 (spatial R2 = 0.89, temporal R2 = 0.91), NO2 (R2 = 0.49, 0.78), and O3 (R2 = 0.67, 0.81) at the national monitors over seven years according to the leave-one-out CV. For the predictions at the local agency monitoring stations, the overall CV R2 was between 0.77 and 0.89 across the air pollutants. We visualized the long-term and seasonal averaged predictions of the PM2.5, NO2, and O3 exposure on maps with a spatial resolution of 100 × 100 m2. Our study provides a useful tool to accurately estimate air pollution exposure with high spatial and temporal resolution at the urban scale. These model predictions will be useful to assess both short-term and long-term air pollution exposure for health studies.

Hygienic standards for chemical agents in ambient air: achievements of the past and modern views into the future

Objective: We generalized and assessed the prior chemical pollutants in accordance with the time of their averaging by the data of domestic and foreign regulatory frameworks. Materials and methods: To achieve the goal, we used the bibliographic and analytical methods. We analyzed the databases of Ukraine and the EU countries, the USA and Canada on the current standards for chemical agents and the time of their averaging in accordance with the purpose - prevention of acute and chronic effects. Results and discussion: As a result of the comparison of the data on the regulations and standards for chemical agents in ambient air, we identified the agents that are criterial ones in most countires. For these substances, monitoring methods have been developed and standards have been substantiated and adopted for two averaging periods: twenty-minute and average daily. In most countries, two standards have been also adopted for each agent, but the averaging period is longer there: an average annual and a daily average. Such standards may control and prevent the chronic effect of substances. As a result, the use of the Ukrainian and foreign standards leads to the controversial estimates of the actual air pollution. The average daily standard adopted in Ukraine, on the one hand, is much stricter in comparison with the similar foreign criteria, and on the other hand, it does not correspond to the peculiarities of the formation of ambient air pollution, and is inadequate for the averaging time. Conclusions: 1. Comparative analysis of hygienic standards indicates the needs to harmonize the ambient air quality standards in force in Ukraine with international ones. 2. The existing system of the hygienic standards in Ukraine must be supplemented with the standard for the annual averaging period by transferring the operating average daily concentrations to this rank.

Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors

Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM2.5. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM2.5 in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM2.5. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM2.5 estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM2.5 spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM2.5 ranged from 1.17 to 3.86 μg/m3. We also found that the clustering of one month characterizing the pattern of PM2.5 distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.

Urban NO x emissions around the world declined faster than anticipated between 2005 and 2019

Emission inventory development for air pollutants, by compiling records from individual emission sources, takes many years and involves extensive multi-national effort. A complementary method to estimate air pollution emissions is in the use of satellite remote sensing. In this study, NO2 observations from the Ozone Monitoring Instrument are combined with re-analysis meteorology to estimate urban nitrogen oxide (NO X ) emissions for 80 global cities between 2005 and 2019. The global average downward trend in satellite-derived urban NOX emissions was 3.1%–4.0% yr−1 between 2009 and 2018 while inventories show a 0%–2.2% yr−1 drop over the same timeframe. This difference is primarily driven by discrepancies between satellite-derived urban NO X emissions and inventories in Africa, China, India, Latin America, and the Middle East. In North America, Europe, Korea, Japan, and Australasia, NOX emissions dropped similarly as reported in the inventories. In Europe, Korea, and Japan only, the temporal trends match the inventories well, but the satellite estimate is consistently larger over time. While many of the discrepancies between satellite-based and inventory emissions estimates represent real differences, some of the discrepancies might be related to the assumptions made to compare the satellite-based estimates with inventory estimates, such as the spatial disaggregation of emissions inventories. Our work identifies that the three largest uncertainties in the satellite estimate are the tropospheric column measurements, wind speed and direction, and spatial definition of each city.